Multi-messenger Astroparticle Physics for the Public via the astroparticle.online Project

Multi-messenger astroparticle physics is still a young field of research and is hardly covered in educational curricula or outreach. The astroparticle.online project, founded in 2018 within the framework of the German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI), encompasses an endeavor to address this issue. Within the project, scientists from Karlsruhe Institute of Technology (KIT), Irkutsk State University (ISU) and Moscow State University (MSU) developed a range of educational materials: articles, video lectures, tests, problems to solve, laboratory works and pre-trained neural networks for particle recognition. The project is supported by the KASCADE Cosmic-ray Data Center (KCDC) and GRADLCI data aggregation platform, where one can retrieve and analyze open scientific data from various experiments. The main audience of the project\u27s activities are high school and undergraduate students. All the educational materials are available online at the project\u27s web portal astroparticle.online, they are used both in online and offline masterclasses organized by the project members, and also as the supplementary content by educational organizations: for example, in the ISU course "Introduction to experimental methods in high energy astrophysics". Over the time the project has been operating, more than 150 students took part in its activities. This contribution will cover the experience gained while running the project for more than 3 years, our challenges and developments

German-Russian Astroparticle Data Life Cycle Initiative to foster Big Data Infrastructure for Multi-Messenger Astronomy

Challenges faced by researchers in multi-messenger astroparticle physics include: computing-intensive search and preprocessing related to the diversity of content and formats of the data from different observatories as well as to data fragmentation over separate storage locations; inconsistencies in user interfaces for data retrieval; lack of the united infrastructure solutions suitable for both data gathering and online analysis, e.g. analyses employing deep neural networks. In order to address solving these issues, the German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI) was created. In addition, we support activities for communicating our research field to the public. The approaches proposed by the project are based on the concept of data life cycle, which assumes a particular pipeline of data curation used for every unit of the data from the moment of its retrieval or creation through the stages of data preprocessing, analysis, publishing and archival. The movement towards unified data curation schemes is essential to increase the benefits gained in the analysis of geographically distributed or content-diverse data. Within the project, an infrastructure for effective astroparticle data curation and online analysis was developed. Using it, first results on deep-learning based analysis were obtained

Additional file 5: of Discrimination of three genetically close Aspergillus species by using high resolution melting analysis applied to indoor air as case study

Melting temperature obtained for the species from the Versicolores group (XLSX 11 kb